Dataset Capstone

To Do

• Fix links

• Make a gif of pymeanshift samples with variables/stats

• Visualize color values

Overview

Summary

Photography has been a hobby of mine for most of my life, and I found a particular niche in abstract photography, specifically multi-exposure images. This background inspired me to find mathematical ways to analyze my photo library as a whole, with a special focus on color trends and affinities.

Business Application

The processes used in this project have a business application within a mobile app. By evaluating a user’s camera roll, the app could discern favorite colors and suggest products that match that color profile.

Data Collection Method

Flickr API

• Worked backwards from the present to separate generic images (downloads, memes, screenshots, videos) from photos (robust EXIF data)
• Collected 4500 usable flickr IDs (more IDs meant a larger date range to sample from)
  • (Python script was used to randomly select from the mega-list)
• Selected IDs had their EXIF data collected (again) and jpg downloaded at “XL” size
• EXIF data was saved to its own CSV

Python

• Each photo was mathematically divided into 9 subimages, to allow for full-photo trends to be compared against center-image trends
  • (All calculations were done on each photo 10 times, once for the full photo, and once for each sub-image. In retrospect, much of the sub-image processing was redundant and could have been gathered via subsetting the full-image matrix)
• Gamma adjustment (RGB linearization)
  • https://en.wikipedia.org/wiki/SRGB#Transfer_function_(%22g`amma%22
• Conversion to HSL for “readable” values (colorsys library)
• Segmentation (“posterization”) (pymeanshift library)
  • https://github.com/fjean/pymeanshift

Note:

Shout out to my buddy Phil! He was a great resource for feedback and encouragement as I formulated my processing script, but also donated runtime on his computer and processed 250 images used in this dataset.

PyMeanShift/Segmentation

A photograph with 4000 pixels may have 4000 different color values represented. I wanted to “clump” pixels with similar colors in the same area of an image into a single color value. PyMeanShift accomplishes this by taking in the image and three numerical variables: spatial radius, range radius, and minimum density. These refer to maximum color difference, maximum placement difference, and minimum “clump” size, respectively.

https://ieeexplore.ieee.org/document/1000236

Datapoints Collected

EXIF

FlickrID - unique identifier for each photo

DateTimeOriginal/CreateDate/ModifyDate - attempted to capture whether the images were edited on the phone (unsuccessful)

Software - iOS version or mobile app used for photo capture

LensInfo/ LensModel - data on which phone lens capture the photo

JFIFVersion - compression marker applied by some 3rd party apps. Disappears when image is edited in native iOS photos app.

ISO - light sensitivity setting

ExposureTime - in seconds (fractions)

FNumber - aperture

FocalLength- Fixed to LensInfo/LensModel

FocalLengthIn35mmFormat - iOS interpretation of zoom level

BrightnessValue - Auto-generated brightness value

SubjectArea -  Coordinate values generated by iOS (not directly relevant to this project, but captured for future use)

Image Data

A python class was used to gather image data as attributes, then dumped to a csv with vars(). 

All relevant attributes/variables described below

using_id - Flickr ID

img_width - in pixels

img_height - in pixels

do_img_at - timestamp for evaluating processing time 

sub_img - 0 for whole image, 1 for top-left, 2 for middle-left, 5 for center, etc.

full_id - concat of flickrID and sub_image to form unique identifier.

RGB Overview Statistics

(r/g/b)_min - (3 columns) Minimum red/green/blue channel value in the whole image

(r/g/b)_max - (3 columns) Maximum red/green/blue channel value

(r/g/b)_mean - (3 columns) Average red/green/blue channel value

(r/g/b)_mode - (3 columns) count of common red/green/blue channel value (forgot to capture its value :facepalm: (in my attempt to capture the value, I neglected to reset the index of the pandas dataSeries))

center_rgb - (tuple) R/G/B value of the pixel mathematically in the center of the image

Next segment of columns captured from segmented/posterized image

post_num_regions - number of color “clumps” after processing

post_top_hsl - (tuple) most common pixel value

post_top_count - quantity of most common pixel value

post_(2-6)_hsl - (5 columns)(tuples) next most common pixel values, in descending order of frequency

post_(2-6)_count - (5 columns) counts for their respective common pixel values

center_hsl - (tuple) HSL value of the pixel mathematically in the center of the image

Hue color banding was done by subjective eyeball measurement

All hues: red, orange, yellow, green, cyan, blue, purple, magenta

full_(hue)_count - count of all pixels that fell within the hue band, regardless of saturation and lightness

visib_(hue)_count - count of pixels in the hue band deemed as “visibly [hue]” (saturation over 40%, lightness between 20% and 75%)

vivid_(hue)_count - count of pixels in the hue band deemed as “vividly [hue]” (saturation over 70%, lightness between 30% and 70%)

Saturation Statistics

sat_min_val - lowest saturation value in image

sat_25_val - 25% quartile value

sat_50_val - median saturation

sat_75_val - 75% quartile value

sat_max_val - most saturation

HSL Mean Values

hue_mean_val - average hue value (not incredibly meaningful on a looping spectrum)

sat_mean_val - average saturation value

light_mean_val - average brightness

Lightness Statistics

light_max_val - brightest value

light_max_count - quantity of pixels within 1.5% (literal) of the max lightness value

light_min_val - darkest value

light_min_count - quantity of pixels within 1.5% (literal) of he minimum lightness value (darkest)

light_25_value - 25% quartile value

light_50_value - median brightness

light_75_value - 75% quartile value

gen_bright_count - quantity of pixels with over 85% lightness

gen_dark_count - quantity of pixels with under 15% brightness

common_hsl_(1-4)_val - (4 columns)(tuple) four most common HSL values

common_hsl_(1-4)_count - (4 columns) quantities of the four most common HSL values

Data Collation

Due to collecting image processing data on multiple computers, multiple files were created for exif and image data – partly by design and party due to occasional read/write conflicts on shared files. All records were gathered into Excel and checked for duplicates before exporting as CSVs.

Hypotheses

H1 - Hue vs Date

\(Ho:\) There is no correlation between time of year and color values

\(Ha:\) Warm color values are more prominent between May and September

H2 - Lightness vs Time

\(Ho:\) There is no correlation between time of day and lightness values

\(Ha:\) Lightness values are higher between 6 am and 6pm

H3 - Saturation vs Subject

\(Ho:\) There is no correlation between saturation and being a picture of my cat

\(Ha:\) Low saturation values are increasingly common over time, especially in central sub-images

H4 - Vividness vs Image Type

\(Ho:\) Vivid ratio (percentage of vivid pixels) is uniformly distributed among all Software types

\(Ha:\) Vivid ratio is consistently highest in Slow Shutter Cam photos without JFIF values

R

Preparing the Data

Imports

Cleaning

Pre Import:
Sub-image data for main images (0) was bugged in the first hours of image processing. This was fixed in Excel during the data collation stage.

EXIF:

• Fix column headers
• Drop columns: date_time_original, modify_date, lens_info, f_number, focal_length
• NA values - JFIF <- 0,  subject_area <- >depends on data type< “0 0 0 0”

# names(exif) <- gsub("([a-z0-9])([A-Z])", "\\1_\\2", names(exif))
# names(exif) <- names(exif) %>% tolower()

exif_tidy <- select(exif, -c(date_time_original, modify_date, lens_info, fnumber, focal_length))
exif_tidy <- replace_na(exif_tidy, list(subject_area = "0 0 0 0", jfifversion = 0))

Img_data:

• Drop columns: flickr, img_loc, the_image, crop_coords, r_mode, b_mode, g_mode, img_height, img_width, do_img_at
• Na values - post_2-6_hsl <- (-1,-1,-1)

imgsd_tidy <- select(img_data, -c(flickr, img_loc, the_image, img_width, img_height, crop_coords, do_img_at, r_mode, b_mode, g_mode))

imgsd_tidy <- replace_na(imgsd_tidy, list(
  post_2_hsl = "(-1, -1, -1)",
  post_3_hsl = "(-1, -1, -1)",
  post_4_hsl = "(-1, -1, -1)",
  post_5_hsl = "(-1, -1, -1)",
  post_6_hsl = "(-1, -1, -1)"
  )
  )

Basic Feature Engineering

EXIF

• Split date_time_original to year, month-day, time columns
• Date column uses a placeholder year so month-to-month comparisons are consistent

# exif_tidy <- exif_tidy %>% separate(create_date, into = c('date', 'time'), sep = " ", remove = TRUE) %>% separate(date, into = c('year', 'month', 'day'), sep = ":") 

exif_tidy$date <- as.Date(paste("1881", exif_tidy$month, exif_tidy$day, sep = "-"), format ="%Y-%m-%d")

Img_data

• Count by flickr id

  • Add count(flickr_id) to EXIF
  • Flag counts under 10
  • Flag counts under 6 (ie: subimage 5 not available)
  • Output list of all flickrIDs with less than 10 records for additional (future) processing

subimg_qty <- imgsd_tidy %>% count(using_id)

To my (happy) surprise, only 5 images have less than 10 results and only 2 have less than 6. In the interest of time, I’m noting these IDs by hand and simply removing them from my working data

good_ids <- subimg_qty[subimg_qty$n >=6, "using_id"]

imgsd_tidy <- imgsd_tidy %>% filter(using_id %in% good_ids$using_id)

exif_tidy <- exif_tidy %>% filter(flickr_id %in% good_ids$using_id)

• Total pixels = full_(hue)_count(s) (dimension data incorrect for first 1400 records)

imgsd_tidy <- imgsd_tidy %>% mutate(total_pixels = full_red_count +
                                      full_orange_count +
                                      full_yellow_count +
                                      full_green_count +
                                      full_cyan_count +
                                      full_blue_count + 
                                      full_purple_count +
                                      full_mag_count)

• Split pixel lists/tuples for 3-d mapping

imgsd_tidy$center_hsl <- str_replace(imgsd_tidy$center_hsl, '\\[|\\]', '')

imgsd_tidy <- imgsd_tidy %>% mutate_all(~ gsub('\\(|\\)', '', .))

imgsd_split <- imgsd_tidy %>% 
  separate(center_rgb, 
           into = c('center_r', 'center_g', 'center_b'), 
           sep = ',') %>%
  separate(post_top_hsl, 
           into = c('post_top_hue', 'post_top_sat', 'post_top_light'), 
           sep = ',') %>%
  separate(post_2_hsl, 
           into = c('post_2_hue', 'post_2_sat', 'post_2_light'), 
           sep = ',') %>%
  separate(post_3_hsl, 
           into = c('post_3_hue', 'post_3_sat', 'post_3_light'), 
           sep = ',') %>%  
  separate(post_4_hsl, 
           into = c('post_4_hue', 'post_4_sat', 'post_4_light'), 
           sep = ',') %>%
  separate(post_5_hsl, 
           into = c('post_5_hue', 'post_5_sat', 'post_5_light'), 
           sep = ',') %>%
  separate(post_6_hsl, 
           into = c('post_6_hue', 'post_6_sat', 'post_6_light'), 
           sep = ',') %>%
  separate(center_hsl, 
           into = c('center_hue', 'center_sat', 'center_light'), 
           sep = ',') %>%
  separate(common_hsl_1_val, 
           into = c('common_hsl_1_hue', 'common_hsl_1_sat', 'common_hsl_1_light'), 
           sep = ',') %>%
  separate(common_hsl_2_val, 
           into = c('common_hsl_2_hue', 'common_hsl_2_sat', 'common_hsl_2_light'), 
           sep = ',') %>%
  separate(common_hsl_3_val, 
           into = c('common_hsl_3_hue', 'common_hsl_3_sat', 'common_hsl_3_light'), 
           sep = ',') %>%
  separate(common_hsl_4_val, 
           into = c('common_hsl_4_hue', 'common_hsl_4_sat', 'common_hsl_4_light'), 
           sep = ',')

I probably should have saved those independently during the python image processing stage.

• To be added as needed:

  • Using total_pixels to calculate specific ratios

Exploration

3D Scatter

https://plotly.com/r/3d-scatter-plots/

• Center rgb

fig <- plot_ly(imgsd_split, x=~center_r, y= ~center_g, z= ~center_b, 
               type = "scatter3d", mode="markers", size = 1, color = ~sub_img)

fig

• Rgb mean

fig <- plot_ly(imgsd_split, x=~r_mean, y= ~g_mean, z= ~b_mean,
               type = "scatter3d", mode="markers", size = 2, color = ~sub_img)

fig

Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

• Hsl mean

fig <- plot_ly(imgsd_split, x=~hue_mean_val, y= ~sat_mean_val, z= ~light_mean_val,
               type = "scatter3d", mode="markers", size = 1, color = ~sub_img)

fig

Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

• Post_top_hsl

fig <- plot_ly(imgsd_split, x=~post_top_hue, y= ~post_top_sat, z= ~post_top_light,
               type = "scatter3d", mode="markers", size = 2, color = ~sub_img)

fig

Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

• Common_hsl_1_vals

fig <- plot_ly(imgsd_split, 
               x=~common_hsl_1_hue, 
               y= ~common_hsl_1_sat, 
               z= ~common_hsl_1_light,
               type = "scatter3d", mode="markers", size = 2, color = ~sub_img)

fig

Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

• Common_hsl_2_vals

fig <- plot_ly(imgsd_split, 
               x=~common_hsl_2_hue, 
               y= ~common_hsl_2_sat, 
               z= ~common_hsl_2_light,
               type = "scatter3d", mode="markers", size = 2, color = ~sub_img)

fig

Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

fig <- plot_ly(imgsd_split, 
               x=~common_hsl_3_hue, 
               y= ~common_hsl_3_sat, 
               z= ~common_hsl_3_light,
               type = "scatter3d", mode="markers", size = 2, color = ~sub_img)

fig

Warning: Ignoring 2 observationsWarning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: Ignoring 2 observationsWarning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

fig <- plot_ly(imgsd_split, 
               x=~common_hsl_4_hue, 
               y= ~common_hsl_4_sat, 
               z= ~common_hsl_4_light,
               type = "scatter3d", mode="markers", size = 2, color = ~sub_img)

fig

Warning: Ignoring 2 observationsWarning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: Ignoring 2 observationsWarning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

2D Scatter

fig <- plot_ly(imgsd_split, 
               x=~common_hsl_4_hue, 
               y= ~common_hsl_4_sat, 
               type = "scatter", mode="markers", size = 2, color = ~sub_img)

• Light min x light max

fig <- plot_ly(imgsd_split, 
               x=~light_min_val, 
               y= ~light_max_val, 
               type = "scatter", mode="markers", size = 2, color = ~sub_img)

fig

Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

• Gen bright x gen dark

imgsd_split$gen_bright_count <- as.integer(imgsd_split$gen_bright_count)


imgsd_split$gen_dark_count <- as.integer(imgsd_split$gen_dark_count)

fig <- plot_ly(imgsd_split, 
               x=~gen_bright_count, 
               y= ~gen_dark_count, 
               type = "scatter", mode="markers", size = 2, color = ~sub_img)

fig

Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

• Sat min x sat max

# imgsd_split$gen_bright_count <- as.integer(imgsd_split$gen_bright_count)


# imgsd_split$gen_dark_count <- as.integer(imgsd_split$gen_dark_count)

fig <- plot_ly(imgsd_split, 
               x=~sat_min_val, 
               y= ~sat_max_val, 
               type = "scatter", mode="markers", size = 2, color = ~sub_img)

fig

Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors
Warning: n too large, allowed maximum for palette Set2 is 8
Returning the palette you asked for with that many colors

1D Histograms

• Vivid %
• Post-num (filter by img segment)
• Exif brightness
• Mean_lightness
• (r/g/b) mean
• Date distribution (with and without year)

0D Pie Charts

• Full color bands
• Vis color bands
• Vivid color bands

Analysis

-trim exif down to id/date-time/software/brightness info

Left join to add date/software data from exif to img_data

*do facets with sub_img numbers*

H1

H2

H3

H4

Conclusion

plot(cars)

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